Project description:This study provides insights into the efficacy of beta-blockers as breast cancer therapeutics.Cell line models of basal-type and estrogen receptor-positive breast cancer were profiled for basal levels of adrenoceptor gene/protein expression, and ADRβ2-mediated cell behaviour including migration, invasion, adhesion, and proliferation in response to adrenoceptor agonist/antagonist treatment. Protein profiling and histology identified response biomarkers and drug targets. Protein profiling revealed the upregulation of the pro-metastatic gene LYPD3 in norepinephrine treated MDA MB 468 cells. Histology confirmed selective LYPD3 expression in clinical primary and metastatic breast tumours. These findings demonstrate that basal-type cancer models show a more aggressive ADRβ2-activated phenotype in the resting and stimulated state, which is attenuated by ADRβ2 inhibition, and explain some of the previous studies that have cast doubt on the value of beta-blocker therapy in breast cancer. These findings suggest that propranolol should be clinically evaluated in patients with basal-type tumours expressing high levels of ADRβ2 and LYPD3.
Project description:Multi-dimensional genomic analysis identifies a class of breast cancer patients with high metastatic outcome and differential response to chemotherapeutic drugs The application of multi-dimensional genomic analyses might provide a more refined risk assessment of breast tumor aggressiveness and improve the selection of patients for personalized medicine. Our study demonstrates the feasibility of using CNAs to predict patient outcome. In combination with gene expression profiles, the clinical implication for such prognostic assays is the identification of breast cancer patients at different risks and sensitivities to chemotherapeutic drugs. Keywords: survival time
Project description:Identify therapeutic vulnerabilities of palbociclib resistance in metastatic breast cancer patient-derived xenograft models and identify key biomarkers that correlate with development of resistance to inform new treatment directions
Project description:This SuperSeries is composed of the following subset Series: GSE13914: Molecular profiling of breast cancer cell lines defines relevant tumor models (aCGH) GSE15361: Molecular profiling of breast cancer cell lines defines relevant tumor models (gene expression) Refer to individual Series
Project description:In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection.
Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML
For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.
Project description:Increasing pre-clinical data suggest that chemotherapy may elicit pro-metastatic responses in breast cancer models. Primary tumours release extracellular vesicles (EVs) that can facilitate the seeding and growth of metastatic cancer cells in distant organs, but the effects of chemotherapy on pro-metastatic EVs are poorly understood. The goal of the project was to analyse the protein content in EVs released by the mouse breast cancer cell line 4T1 after treatment with the chemotherapeutic agent paclitaxel (PTX) or its vehicle control cremophor (CREMO).
Project description:Targeting the PI3K-AKT-mTOR pathway is a promising therapeutic strategy for breast cancer treatment. However, low response rates and the development of acquired resistance to PI3K-AKT-mTOR inhibitors remain major challenges for successful patient treatment. Here, we show that MYC activation is a central and clinically relevant mechanism of resistance to mTOR inhibitors (mTORi) in breast cancer. Multi-omic profiling of mouse invasive lobular carcinoma (ILC) tumors revealed recurrent focal Myc amplification in tumors that acquire resistance to the mTORi AZD8055. The gained MYC activity was significantly associated with biological processes linked to mTORi response. Specifically, MYC counteracted the translation inhibitory effect induced by mTORi by promoting the translation of ribosomal proteins. In vitro and in vivo induction of MYC conferred resistance to AZD8055 as well as the clinically approved mTORi everolimus, both in mouse models of ILC and human breast cancer models. Conversely, AZD8055-resistant ILC cells depended on MYC, as demonstrated by synergistic growth inhibition using mTORi and MYCi combination treatment. Notably, MYC status was significantly associated with poor response to everolimus therapy in metastatic breast cancer patients. Thus, MYC is a clinically relevant determinant of mTORi resistance that may guide the selection of breast cancer patients for mTOR targeted therapies.